This paper proposes and illustrates an AI embedded object-oriented methodology to formulate the computable general equilibrium (CGE) models. In this framework, a CGE model is viewed as a collection of objects embedd...This paper proposes and illustrates an AI embedded object-oriented methodology to formulate the computable general equilibrium (CGE) models. In this framework, a CGE model is viewed as a collection of objects embedded AI or namely agents in computer world, corresponding to economic agents and entities in real world, such as government, households, markets and so on. A frame representation of major objects in CGE model is used for trade and environment. Embedded Al object-oriented approach (or software agent) is used in the CGE model representation can able to narrow the gap among the semantic representation, formal CGE (mathematical) representation and computer and algorithm representation, and to improve CGE in understanding and maintenance etc. In such a system, constructing a CGE model to appear an intuitive process rather than an abstract process. This intuitive process needs more understanding of the substance of economics and the logic underlying the problem rather than mathematical notation.展开更多
Two Co(Ⅱ)and Ni(Ⅱ)complexes were synthesized by synergistic coordination of 3,3-diphenylpropionic acid(HDPA)and 2,2′-bipyridylamine(PAm).The structures of complexes[Co(DPA)_(2)(PAm)]·2H_(2)O(1)and[Ni(DPA)_(2)(...Two Co(Ⅱ)and Ni(Ⅱ)complexes were synthesized by synergistic coordination of 3,3-diphenylpropionic acid(HDPA)and 2,2′-bipyridylamine(PAm).The structures of complexes[Co(DPA)_(2)(PAm)]·2H_(2)O(1)and[Ni(DPA)_(2)(PAm)]·2H_(2)O(2)were determined by single-crystal X-ray diffraction,IR spectroscopy,and powder X-ray diffraction.Hirshfeld surface analysis provided quantitative insights into the intermolecular interactions within the complexes,while molecular docking studies elucidated their binding modes and affinities toward urease.Furthermore,the biological activities of both complexes were systematically evaluated through a range of assays,including DNA binding,urease inhibition,antibacterial activity,and in vitro cytotoxicity against cancer cells.Both complexes exhibited binding affinity for DNA and displayed notable urease inhibitory activity.Under in vitro conditions,both complexes showed appreciable cytotoxicity toward HepG2 cells with efficacy comparable to clinically used platinumbased anticancer agents.CCDC:2479943,1;2479944,2.展开更多
Biomass-based hydrocarbon fuels,as one of the alternatives to traditional fossil fuels,have attracted considerable attention in the energy field due to their renewability and environmental benefits.This article provid...Biomass-based hydrocarbon fuels,as one of the alternatives to traditional fossil fuels,have attracted considerable attention in the energy field due to their renewability and environmental benefits.This article provides a systematic review of recent research progress in the chemical synthesis of biomass-based hydrocarbon fuels.It outlines the conversion pathways using feedstocks such as lipids,terpenoids,cellulose/hemicellulose,and lignin.Depending on the feedstock,various products with distinct structural characteristics can be prepared through reactions such as cyclization,condensation,and catalytic hydrogenation.Throughout the synthesis process,three key factors play a critical role:efficient catalyst development,production process optimization,and computational-chemistry-based molecular design.Finally,the article discusses future perspectives for biomass-based hydrocarbon fuel synthesis research.展开更多
Redistribution Layer(RDL),composed of layered dielectrics and electroplated copper materials,is a basic structure to rearrange numerous I/O pads on the chip surface in wafer-level advanced packaging.As the key chemica...Redistribution Layer(RDL),composed of layered dielectrics and electroplated copper materials,is a basic structure to rearrange numerous I/O pads on the chip surface in wafer-level advanced packaging.As the key chemicals in electrolyte baths,electroplating additives have undergone continuous development to meet the industrial needs for high-speed and fine-line/fine-pitch applications.Meanwhile,the intricate relationships between additive chemical structures and electroplated copper properties are yet to be well understood.In this work,a pair of triphenylmethane-based dye molecules,i.e.,gentian violet(GV)and methyl green(MG),was comparatively investigated as levelers for high-speed RDL copper electroplating.Compared to GV,significantly stronger electrochemical polarization and tunable deposit morphology can be achieved by MG with just one extra quaternized amine terminal.Combining quantum chemical computations,in situ spectroelectrochemical analyses,and microstructural characterization,it is found that MG possesses enhanced electrostatic adsorption,surface coverage and multi-additive synergies,enabling tailored copper trace morphology.This study elaborates the adsorption mechanism and screening criteria of triphenylmethane-derived levelers,and presents a candidate additive structure for high-speed copper electroplating.展开更多
The capture of atmospheric carbon dioxide by adsorbents is an important strategy to deal with the greenhouse effect.Compared with traditional CO_(2) adsorption materials like activated carbon,silica gel,and zeolite mo...The capture of atmospheric carbon dioxide by adsorbents is an important strategy to deal with the greenhouse effect.Compared with traditional CO_(2) adsorption materials like activated carbon,silica gel,and zeolite molecular sieves,covalent organic frameworks(COFs)have excellent thermal and chemical stabilities and can be produced in many different forms.Using their different possible construction units,ordered structures for specific applications can be produced,giving them broad prospects in fields such as gas storage.This review analyzes the different types of COFs that have been synthesized and their different methods of CO_(2) capture.It then discusses different ways to increase CO_(2) adsorption by changing the internal structure of COFs and modifying their surfaces.The limitations of COF-derived carbon materials in CO_(2) capture are reviewed and,finally,the key role of machine learning and computational simulation in improving CO_(2) adsorption is mentioned,and the current status and future possible uses of COFs are summarized.展开更多
This comprehensive study investigates the formation and evolution of intermetallic compounds during the solidification process of magnesium alloys using advanced micro X-ray computed tomography.By analyzing both commo...This comprehensive study investigates the formation and evolution of intermetallic compounds during the solidification process of magnesium alloys using advanced micro X-ray computed tomography.By analyzing both common industrial Mg-Al-Zn alloys and a novel rare earth-containing Mg-Ni-Gd-Y alloy,we aim to characterize the nucleation,growth,and distribution of Al-Mn and eutectic intermetallics across various stages of solidification.The non destructive imaging technique employed in this research provides high-resolution,three-dimensional insights into the microstructural development,allowing for a detailed examination of the morphology,spatial arrangement,and interconnectivity of intermetallic phases.This approach overcomes limitations of traditional two-dimensional metallographic methods,offering a more comprehensive understanding of the complex three-dimensional structures formed during solidification.展开更多
In underwater target search path planning,the accuracy of sonar models directly dictates the accurate assessment of search coverage.In contrast to physics-informed sonar models,traditional geometric sonar models fail ...In underwater target search path planning,the accuracy of sonar models directly dictates the accurate assessment of search coverage.In contrast to physics-informed sonar models,traditional geometric sonar models fail to accurately characterize the complex influence of marine environments.To overcome these challenges,we propose an acoustic physics-informed intelligent path planning framework for underwater target search,integrating three core modules:The acoustic-physical modeling module adopts 3D ray-tracing theory and the active sonar equation to construct a physics-driven sonar detection model,explicitly accounting for environmental factors that influence sonar performance across heterogeneous spaces.The hybrid parallel computing module adopts a message passing interface(MPI)/open multi-processing(Open MP)hybrid strategy for large-scale acoustic simulations,combining computational domain decomposition and physics-intensive task acceleration.The search path optimization module adopts the covariance matrix adaptation evolution algorithm to solve continuous optimization problems of heading angles,which ensures maximum search coverage for targets.Largescale experiments conducted in the Pacific and Atlantic Oceans demonstrate the framework's effectiveness:(1)Precise capture of sonar detection range variations from 5.45 km to 50 km in heterogeneous marine environments.(2)Significant speedup of 453.43×for acoustic physics modeling through hybrid parallelization.(3)Notable improvements of 7.23%in detection coverage and 15.86%reduction in optimization time compared to the optimal baseline method.The framework provides a robust solution for underwater search missions in complex marine environments.展开更多
As a non-contact ultra-precision machining method,abrasive water jet polishing(AWJP)has signi-ficant application in optical elements processing due to its stable tool influence function(TIF),no subsurface damage and s...As a non-contact ultra-precision machining method,abrasive water jet polishing(AWJP)has signi-ficant application in optical elements processing due to its stable tool influence function(TIF),no subsurface damage and strong adaptability to workpiece shapes.In this study,the effects of jet pressure,nozzle diameter and impinging angle on the distribution of pressure,velocity and wall shear stress in the polishing flow field were systematically analyzed by computational fluid dynamics(CFD)simulation.Based on the Box-Behnken experimental design,a response surface regression model was constructed to investigate the influence mech-anism of process parameters on material removal rate(MRR)and surface roughness(Ra)of fused silica.And experimental results showed that increasing jet pressure and nozzle diameter significantly improved MRR,consistent with shear stress distribution revealed by CFD simulations.However,increasing jet pressure and impinging angle caused higher Ra values,which was unfavorable for surface quality improvement.Genetic algorithm(GA)was used for multi-objective optimization to establish Pareto solutions,achieving concurrent optimization of polishing efficiency and surface quality.A parameter combination of 2 MPa jet pressure,0.3 mm nozzle diameter,and 30°impinging angle achieved MRR of 169.05μm^(3)/s and Ra of 0.50 nm.Exper-imental verification showed prediction errors of 4.4%(MRR)and 3.8%(Ra),confirming the model’s reliabil-ity.This parameter optimization system provides theoretical basis and technical support for ultra-precision polishing of complex curved optical components.展开更多
In order to improve the efficiency of cloud-based web services,an improved plant growth simulation algorithm scheduling model.This model first used mathematical methods to describe the relationships between cloud-base...In order to improve the efficiency of cloud-based web services,an improved plant growth simulation algorithm scheduling model.This model first used mathematical methods to describe the relationships between cloud-based web services and the constraints of system resources.Then,a light-induced plant growth simulation algorithm was established.The performance of the algorithm was compared through several plant types,and the best plant model was selected as the setting for the system.Experimental results show that when the number of test cloud-based web services reaches 2048,the model being 2.14 times faster than PSO,2.8 times faster than the ant colony algorithm,2.9 times faster than the bee colony algorithm,and a remarkable 8.38 times faster than the genetic algorithm.展开更多
Risk management often plays an important role in decision making un-der uncertainty.In quantitative risk management,assessing and optimizing risk metrics requires eficient computing techniques and reliable theoretical...Risk management often plays an important role in decision making un-der uncertainty.In quantitative risk management,assessing and optimizing risk metrics requires eficient computing techniques and reliable theoretical guarantees.In this pa-per,we introduce several topics on quantitative risk management and review some of the recent studies and advancements on the topics.We consider several risk metrics and study decision models that involve the metrics,with a main focus on the related com-puting techniques and theoretical properties.We show that stochastic optimization,as a powerful tool,can be leveraged to effectively address these problems.展开更多
Ultrasound computed tomography(USCT)is a noninvasive biomedical imaging modality that offers insights into acoustic properties such as the sound speed(SS)and acoustic attenuation(AA)of the human body,enhancing diagnos...Ultrasound computed tomography(USCT)is a noninvasive biomedical imaging modality that offers insights into acoustic properties such as the sound speed(SS)and acoustic attenuation(AA)of the human body,enhancing diagnostic accuracy and therapy planning.Full waveform inversion(FWI)is a promising USCT image reconstruction method that optimizes the parameter fields of a wave propagation model via gradient-based optimization.However,twodimensional FWI methods are limited by their inability to account for three-dimensional wave propagation in the elevation direction,resulting in image artifacts.To address this problem,we propose a three-dimensional time-domain full waveform inversion algorithm to reconstruct the SS and AA distributions on the basis of a fractional Laplacian wave equation,adjoint field formulation,and gradient descent optimization.Validated by two sets of simulations,the proposed algorithm has potential for generating high-resolution and quantitative SS and AA distributions.This approach holds promise for clinical USCT applications,assisting early disease detection,precise abnormality localization,and optimized treatment planning,thus contributing to better healthcare outcomes.展开更多
Fourier Ptychographic Microscopy(FPM)is a high-throughput computational optical imaging technology reported in 2013.It effectively breaks through the trade-off between high-resolution imaging and wide-field imaging.In...Fourier Ptychographic Microscopy(FPM)is a high-throughput computational optical imaging technology reported in 2013.It effectively breaks through the trade-off between high-resolution imaging and wide-field imaging.In recent years,it has been found that FPM is not only a tool to break through the trade-off between field of view and spatial resolution,but also a paradigm to break through those trade-off problems,thus attracting extensive attention.Compared with previous reviews,this review does not introduce its concept,basic principles,optical system and series of applications once again,but focuses on elaborating the three major difficulties faced by FPM technology in the process from“looking good”in the laboratory to“working well”in practical applications:mismatch between numerical model and physical reality,long reconstruction time and high computing power demand,and lack of multi-modal expansion.It introduces how to achieve key technological innovations in FPM through the dual drive of Artificial Intelligence(AI)and physics,including intelligent reconstruction algorithms introducing machine learning concepts,optical-algorithm co-design,fusion of frequency domain extrapolation methods and generative adversarial networks,multi-modal imaging schemes and data fusion enhancement,etc.,gradually solving the difficulties of FPM technology.Conversely,this review deeply considers the unique value of FPM technology in potentially feeding back to the development of“AI+optics”,such as providing AI benchmark tests under physical constraints,inspirations for the balance of computing power and bandwidth in miniaturized intelligent microscopes,and photoelectric hybrid architectures.Finally,it introduces the industrialization path and frontier directions of FPM technology,pointing out that with the promotion of the dual drive of AI and physics,it will generate a large number of industrial application case,and looks forward to the possibilities of future application scenarios and expansions,for instance,body fluid biopsy and point-of-care testing at the grassroots level represent the expansion of the growth market.展开更多
In this paper,a new technique is introduced to construct higher-order iterative methods for solving nonlinear systems.The order of convergence of some iterative methods can be improved by three at the cost of introduc...In this paper,a new technique is introduced to construct higher-order iterative methods for solving nonlinear systems.The order of convergence of some iterative methods can be improved by three at the cost of introducing only one additional evaluation of the function in each step.Furthermore,some new efficient methods with a higher-order of convergence are obtained by using only a single matrix inversion in each iteration.Analyses of convergence properties and computational efficiency of these new methods are made and testified by several numerical problems.By comparison,the new schemes are more efficient than the corresponding existing ones,particularly for large problem sizes.展开更多
The bipolar plate serves a crucial function in the PEMFC regarding fuel supply quality and electrochemical performance.Achieving uniform gas distribution in the flow field presents significant challenges,particularly ...The bipolar plate serves a crucial function in the PEMFC regarding fuel supply quality and electrochemical performance.Achieving uniform gas distribution in the flow field presents significant challenges,particularly in transition areas where channel configurations directly affect reactant gas transport.This study presents a novel cathode flow field design for bipolar plates featuring multiple parallel sub-channels,analyzed through computational fluid dynamics modeling and simulation,with particular attention to the Coanda effect in the transition area.The investigation evaluates and analyzes mass flow distribution and pressure loss characteristics.The flow channels undergo further refinement through optimization of the transition region structure to achieve uniform mass flow distribution and reduced pressure loss.The results demonstrate that channel width allocation based on uniform distribution principles enhances mass flow uniformity.Additionally,incorporating fillets in the transition region with a jet width to fillet radius ratio below 0.5 amplifies the Coanda effect,thereby reducing adverse pressure gradients and flow separation phenomena,resulting in decreased pressure drop.Furthermore,when modifying gas outlet pressure to minimize pressure loss,an adjustment strategy for gas reactant concentration maintains consistent mass flow rates.The findings provide valuable reference points and optimization guidelines for flow channel design in bipolar plates,considering both fluid distribution uniformity and pressure loss factors.展开更多
Federated learning(FL)is a distributed machine learning paradigm for edge cloud computing.FL can facilitate data-driven decision-making in tactical scenarios,effectively addressing both data volume and infrastructure ...Federated learning(FL)is a distributed machine learning paradigm for edge cloud computing.FL can facilitate data-driven decision-making in tactical scenarios,effectively addressing both data volume and infrastructure challenges in edge environments.However,the diversity of clients in edge cloud computing presents significant challenges for FL.Personalized federated learning(pFL)received considerable attention in recent years.One example of pFL involves exploiting the global and local information in the local model.Current pFL algorithms experience limitations such as slow convergence speed,catastrophic forgetting,and poor performance in complex tasks,which still have significant shortcomings compared to the centralized learning.To achieve high pFL performance,we propose FedCLCC:Federated Contrastive Learning and Conditional Computing.The core of FedCLCC is the use of contrastive learning and conditional computing.Contrastive learning determines the feature representation similarity to adjust the local model.Conditional computing separates the global and local information and feeds it to their corresponding heads for global and local handling.Our comprehensive experiments demonstrate that FedCLCC outperforms other state-of-the-art FL algorithms.展开更多
Trans-medium flight vehicles can combine high aerial maneuverability and underwater concealment ability,which have attracted much attention recently.As the most crucial procedure,the trajectory design generally determ...Trans-medium flight vehicles can combine high aerial maneuverability and underwater concealment ability,which have attracted much attention recently.As the most crucial procedure,the trajectory design generally determines the trans-medium flight vehicle performance.To quantitatively analyze the flight vehicle performance,an entire aerial-aquatic trajectory model is developed in this paper.Different from modeling a trajectory purely for the water entry process,the constructed entire trajectory model has integrated aerial,water entry,and underwater trajectories together,which can consider the influence of the connected trajectories.As for the aerial and underwater trajectories,explicit dynamic models are established to obtain the trajectory parameters.Due to the complicated fluid force during high-velocity water entry,a computational fluid dynamics model is investigated to analyze this phase.The compu-tational domain size is adaptively refined according to the final aerial trajectory state,where the redundant computational domain is removed.An entire trajectory optimization problem is then formulated to maximize the total flight range via tuning the joint states of different trajectories.Simultaneously,several constraints,i.e.,the max impact load,trajectory height,etc.,are involved in the optimization problem.Rather than directly optimizing by a heuristic algorithm,a multi-surrogate cooperative sampling-based optimization method is proposed to alleviate the computational complexity of the entire trajectory optimization problem.In this method,various surrogates coopera-tively generate infill sample points,thereby preventing the poor approximation.After optimization,the total flight range can be improved by 20%,while all the constraints are satisfied.The result demonstrates the effectiveness and practicability of the developed model and optimization framework.展开更多
With increasingly stringent requirements for the airtightness of high-speed train bodies,determining appropriate airtightness levels has become critically important.To calculate the airtightness of high-speed train bo...With increasingly stringent requirements for the airtightness of high-speed train bodies,determining appropriate airtightness levels has become critically important.To calculate the airtightness of high-speed train bodies more accurately,based on one-dimensional isentropic flow theory,this study derives cabin pressure calculation models for both positive and negative pressure conditions during static airtightness tests of high-speed train bodies.Since the flow coefficient,which is closely related to the leakage characteristics of the carriage,is influenced by multiple factors including operating pressure conditions(positive/negative),leakage path cross-sectional shape,and size,a flow coefficient calibration method is proposed to achieve high-precision and efficient calibration of the flow coefficient for trains with varying leakage properties.This method generates a series of flow coefficient values for circular and square cross-sectional shapes under both positive and negative pressure conditions across various cross-sectional areas.Furthermore,functional relationships between flow coefficient and leakage path area under positive/negative pressure are established through curve fitting.Using these functional relationships and the cabin pressure calculation model,the pressure variation curves for a static airtightness test are simulated.Specifically,for circular cross-sectional shapes,the theoretical curves under positive and negative pressure conditions exhibited R^(2) values of 0.9936 and 0.9931,respectively,when compared to experimental data,and for square cross-sectional shapes,the corresponding R^(2) values are 0.9928 and 0.9932,validating the accuracy of the proposed theoretical model.The proposed theoretical model effectively evaluates the airtightness of high-speed train bodies with varying performance levels during static airtightness tests,providing a robust theoretical reference for optimizing high-speed train airtightness design.展开更多
The suspension gap is a critical operational parameter for high-speed maglev trains and significantly impacts their aerodynamic performance.Based on an engineering prototype of the high-temperature superconducting(HTS...The suspension gap is a critical operational parameter for high-speed maglev trains and significantly impacts their aerodynamic performance.Based on an engineering prototype of the high-temperature superconducting(HTS)pinning maglev train,this study established a detailed three-dimensional model,and then the aerodynamic characteristics of the HTS maglev train at 600 km/h with suspension gaps of 10 mm,20 mm,and 30 mm were simulated based on the improved delayed detached eddy simulation(IDDES)turbulence model and SST kωtwo-equation.The results demonstrated that the underbody design of the HTS maglev train leads to unique aerodynamic drag and aerothermal distribution phenomena.The head car experiences the smallest drag,while the tail car experiences the largest.The aerothermal temperature on the train's bottom surface progressively increases from the head to the tail.Additionally,the U-shaped track significantly constrains the flow around the train body,forming strong vortex structures.As the suspension gap increases from 10 mm to 30 mm,the airflow velocity in the train-track gap rises,reducing the underbody pressure and decreasing the lift of the head car by 12.43%.The drag of the head car increases by 10.98%,primarily due to changes in pressure drag.Additionally,the temperature at the underbody of the tail car rises further due to significant airflow deceleration.These findings provide valuable insights for advancing the engineering design and application of the high-speed HTS maglev technology.展开更多
文摘This paper proposes and illustrates an AI embedded object-oriented methodology to formulate the computable general equilibrium (CGE) models. In this framework, a CGE model is viewed as a collection of objects embedded AI or namely agents in computer world, corresponding to economic agents and entities in real world, such as government, households, markets and so on. A frame representation of major objects in CGE model is used for trade and environment. Embedded Al object-oriented approach (or software agent) is used in the CGE model representation can able to narrow the gap among the semantic representation, formal CGE (mathematical) representation and computer and algorithm representation, and to improve CGE in understanding and maintenance etc. In such a system, constructing a CGE model to appear an intuitive process rather than an abstract process. This intuitive process needs more understanding of the substance of economics and the logic underlying the problem rather than mathematical notation.
文摘Two Co(Ⅱ)and Ni(Ⅱ)complexes were synthesized by synergistic coordination of 3,3-diphenylpropionic acid(HDPA)and 2,2′-bipyridylamine(PAm).The structures of complexes[Co(DPA)_(2)(PAm)]·2H_(2)O(1)and[Ni(DPA)_(2)(PAm)]·2H_(2)O(2)were determined by single-crystal X-ray diffraction,IR spectroscopy,and powder X-ray diffraction.Hirshfeld surface analysis provided quantitative insights into the intermolecular interactions within the complexes,while molecular docking studies elucidated their binding modes and affinities toward urease.Furthermore,the biological activities of both complexes were systematically evaluated through a range of assays,including DNA binding,urease inhibition,antibacterial activity,and in vitro cytotoxicity against cancer cells.Both complexes exhibited binding affinity for DNA and displayed notable urease inhibitory activity.Under in vitro conditions,both complexes showed appreciable cytotoxicity toward HepG2 cells with efficacy comparable to clinically used platinumbased anticancer agents.CCDC:2479943,1;2479944,2.
基金Support by National Natural Science Foundation of China(22127802,22573091)the HY Action(62402010305)。
文摘Biomass-based hydrocarbon fuels,as one of the alternatives to traditional fossil fuels,have attracted considerable attention in the energy field due to their renewability and environmental benefits.This article provides a systematic review of recent research progress in the chemical synthesis of biomass-based hydrocarbon fuels.It outlines the conversion pathways using feedstocks such as lipids,terpenoids,cellulose/hemicellulose,and lignin.Depending on the feedstock,various products with distinct structural characteristics can be prepared through reactions such as cyclization,condensation,and catalytic hydrogenation.Throughout the synthesis process,three key factors play a critical role:efficient catalyst development,production process optimization,and computational-chemistry-based molecular design.Finally,the article discusses future perspectives for biomass-based hydrocarbon fuel synthesis research.
基金National Natural Science Foundation of China(Grant Nos.62304143,22425204,U22A20396 and 22288102)National Key Research and Development Program of China(Grants No.2023YFA1507202)the autonomous deployment project of State Key Laboratory of Materials for Integrated Circuits(No.SKLJC-Z2024-C03)。
文摘Redistribution Layer(RDL),composed of layered dielectrics and electroplated copper materials,is a basic structure to rearrange numerous I/O pads on the chip surface in wafer-level advanced packaging.As the key chemicals in electrolyte baths,electroplating additives have undergone continuous development to meet the industrial needs for high-speed and fine-line/fine-pitch applications.Meanwhile,the intricate relationships between additive chemical structures and electroplated copper properties are yet to be well understood.In this work,a pair of triphenylmethane-based dye molecules,i.e.,gentian violet(GV)and methyl green(MG),was comparatively investigated as levelers for high-speed RDL copper electroplating.Compared to GV,significantly stronger electrochemical polarization and tunable deposit morphology can be achieved by MG with just one extra quaternized amine terminal.Combining quantum chemical computations,in situ spectroelectrochemical analyses,and microstructural characterization,it is found that MG possesses enhanced electrostatic adsorption,surface coverage and multi-additive synergies,enabling tailored copper trace morphology.This study elaborates the adsorption mechanism and screening criteria of triphenylmethane-derived levelers,and presents a candidate additive structure for high-speed copper electroplating.
文摘The capture of atmospheric carbon dioxide by adsorbents is an important strategy to deal with the greenhouse effect.Compared with traditional CO_(2) adsorption materials like activated carbon,silica gel,and zeolite molecular sieves,covalent organic frameworks(COFs)have excellent thermal and chemical stabilities and can be produced in many different forms.Using their different possible construction units,ordered structures for specific applications can be produced,giving them broad prospects in fields such as gas storage.This review analyzes the different types of COFs that have been synthesized and their different methods of CO_(2) capture.It then discusses different ways to increase CO_(2) adsorption by changing the internal structure of COFs and modifying their surfaces.The limitations of COF-derived carbon materials in CO_(2) capture are reviewed and,finally,the key role of machine learning and computational simulation in improving CO_(2) adsorption is mentioned,and the current status and future possible uses of COFs are summarized.
基金Project(2023YFB4606200)supported by the National Key Research and Development Program of ChinaProject(2023-SSRF-HZ-503114-2)supported by Shanghai Synchrotron Radiation Facility,Instrument BL16U2,China。
文摘This comprehensive study investigates the formation and evolution of intermetallic compounds during the solidification process of magnesium alloys using advanced micro X-ray computed tomography.By analyzing both common industrial Mg-Al-Zn alloys and a novel rare earth-containing Mg-Ni-Gd-Y alloy,we aim to characterize the nucleation,growth,and distribution of Al-Mn and eutectic intermetallics across various stages of solidification.The non destructive imaging technique employed in this research provides high-resolution,three-dimensional insights into the microstructural development,allowing for a detailed examination of the morphology,spatial arrangement,and interconnectivity of intermetallic phases.This approach overcomes limitations of traditional two-dimensional metallographic methods,offering a more comprehensive understanding of the complex three-dimensional structures formed during solidification.
基金supported by Natural Science Foundation of Hu'nan Province(2024JJ5409)。
文摘In underwater target search path planning,the accuracy of sonar models directly dictates the accurate assessment of search coverage.In contrast to physics-informed sonar models,traditional geometric sonar models fail to accurately characterize the complex influence of marine environments.To overcome these challenges,we propose an acoustic physics-informed intelligent path planning framework for underwater target search,integrating three core modules:The acoustic-physical modeling module adopts 3D ray-tracing theory and the active sonar equation to construct a physics-driven sonar detection model,explicitly accounting for environmental factors that influence sonar performance across heterogeneous spaces.The hybrid parallel computing module adopts a message passing interface(MPI)/open multi-processing(Open MP)hybrid strategy for large-scale acoustic simulations,combining computational domain decomposition and physics-intensive task acceleration.The search path optimization module adopts the covariance matrix adaptation evolution algorithm to solve continuous optimization problems of heading angles,which ensures maximum search coverage for targets.Largescale experiments conducted in the Pacific and Atlantic Oceans demonstrate the framework's effectiveness:(1)Precise capture of sonar detection range variations from 5.45 km to 50 km in heterogeneous marine environments.(2)Significant speedup of 453.43×for acoustic physics modeling through hybrid parallelization.(3)Notable improvements of 7.23%in detection coverage and 15.86%reduction in optimization time compared to the optimal baseline method.The framework provides a robust solution for underwater search missions in complex marine environments.
文摘As a non-contact ultra-precision machining method,abrasive water jet polishing(AWJP)has signi-ficant application in optical elements processing due to its stable tool influence function(TIF),no subsurface damage and strong adaptability to workpiece shapes.In this study,the effects of jet pressure,nozzle diameter and impinging angle on the distribution of pressure,velocity and wall shear stress in the polishing flow field were systematically analyzed by computational fluid dynamics(CFD)simulation.Based on the Box-Behnken experimental design,a response surface regression model was constructed to investigate the influence mech-anism of process parameters on material removal rate(MRR)and surface roughness(Ra)of fused silica.And experimental results showed that increasing jet pressure and nozzle diameter significantly improved MRR,consistent with shear stress distribution revealed by CFD simulations.However,increasing jet pressure and impinging angle caused higher Ra values,which was unfavorable for surface quality improvement.Genetic algorithm(GA)was used for multi-objective optimization to establish Pareto solutions,achieving concurrent optimization of polishing efficiency and surface quality.A parameter combination of 2 MPa jet pressure,0.3 mm nozzle diameter,and 30°impinging angle achieved MRR of 169.05μm^(3)/s and Ra of 0.50 nm.Exper-imental verification showed prediction errors of 4.4%(MRR)and 3.8%(Ra),confirming the model’s reliabil-ity.This parameter optimization system provides theoretical basis and technical support for ultra-precision polishing of complex curved optical components.
基金Shanxi Province Higher Education Science and Technology Innovation Fund Project(2022-676)Shanxi Soft Science Program Research Fund Project(2016041008-6)。
文摘In order to improve the efficiency of cloud-based web services,an improved plant growth simulation algorithm scheduling model.This model first used mathematical methods to describe the relationships between cloud-based web services and the constraints of system resources.Then,a light-induced plant growth simulation algorithm was established.The performance of the algorithm was compared through several plant types,and the best plant model was selected as the setting for the system.Experimental results show that when the number of test cloud-based web services reaches 2048,the model being 2.14 times faster than PSO,2.8 times faster than the ant colony algorithm,2.9 times faster than the bee colony algorithm,and a remarkable 8.38 times faster than the genetic algorithm.
文摘Risk management often plays an important role in decision making un-der uncertainty.In quantitative risk management,assessing and optimizing risk metrics requires eficient computing techniques and reliable theoretical guarantees.In this pa-per,we introduce several topics on quantitative risk management and review some of the recent studies and advancements on the topics.We consider several risk metrics and study decision models that involve the metrics,with a main focus on the related com-puting techniques and theoretical properties.We show that stochastic optimization,as a powerful tool,can be leveraged to effectively address these problems.
基金supported by the National Key Research and Development Program of China(2022YFA1404400)the National Natural Science Foundation of China(62122072,12174368,61705216,62405306)+4 种基金Anhui Provincial Department of Science and Technology(202203a07020020,18030801138)Anhui Provincial Natural Science Foundation(2308085QA21,2408085QF187)the USTC Research Funds of the Double First-Class Initiative(YD2090002015)the Institute of Artificial Intelligence at Hefei Comprehensive National Science Center(23YGXT005)the Fundamental Research Funds for the Central Universities(WK2090000083).
文摘Ultrasound computed tomography(USCT)is a noninvasive biomedical imaging modality that offers insights into acoustic properties such as the sound speed(SS)and acoustic attenuation(AA)of the human body,enhancing diagnostic accuracy and therapy planning.Full waveform inversion(FWI)is a promising USCT image reconstruction method that optimizes the parameter fields of a wave propagation model via gradient-based optimization.However,twodimensional FWI methods are limited by their inability to account for three-dimensional wave propagation in the elevation direction,resulting in image artifacts.To address this problem,we propose a three-dimensional time-domain full waveform inversion algorithm to reconstruct the SS and AA distributions on the basis of a fractional Laplacian wave equation,adjoint field formulation,and gradient descent optimization.Validated by two sets of simulations,the proposed algorithm has potential for generating high-resolution and quantitative SS and AA distributions.This approach holds promise for clinical USCT applications,assisting early disease detection,precise abnormality localization,and optimized treatment planning,thus contributing to better healthcare outcomes.
基金National Natural Science Foundation of China(No.12574332)the Space Optoelectronic Measurement and Perception Lab.,Beijing Institute of Control Engineering(No.LabSOMP-2023-10)Major Science and Technology Innovation Program of Xianyang City(No.L2024-ZDKJ-ZDCGZH-0021)。
文摘Fourier Ptychographic Microscopy(FPM)is a high-throughput computational optical imaging technology reported in 2013.It effectively breaks through the trade-off between high-resolution imaging and wide-field imaging.In recent years,it has been found that FPM is not only a tool to break through the trade-off between field of view and spatial resolution,but also a paradigm to break through those trade-off problems,thus attracting extensive attention.Compared with previous reviews,this review does not introduce its concept,basic principles,optical system and series of applications once again,but focuses on elaborating the three major difficulties faced by FPM technology in the process from“looking good”in the laboratory to“working well”in practical applications:mismatch between numerical model and physical reality,long reconstruction time and high computing power demand,and lack of multi-modal expansion.It introduces how to achieve key technological innovations in FPM through the dual drive of Artificial Intelligence(AI)and physics,including intelligent reconstruction algorithms introducing machine learning concepts,optical-algorithm co-design,fusion of frequency domain extrapolation methods and generative adversarial networks,multi-modal imaging schemes and data fusion enhancement,etc.,gradually solving the difficulties of FPM technology.Conversely,this review deeply considers the unique value of FPM technology in potentially feeding back to the development of“AI+optics”,such as providing AI benchmark tests under physical constraints,inspirations for the balance of computing power and bandwidth in miniaturized intelligent microscopes,and photoelectric hybrid architectures.Finally,it introduces the industrialization path and frontier directions of FPM technology,pointing out that with the promotion of the dual drive of AI and physics,it will generate a large number of industrial application case,and looks forward to the possibilities of future application scenarios and expansions,for instance,body fluid biopsy and point-of-care testing at the grassroots level represent the expansion of the growth market.
基金Supported by the National Natural Science Foundation of China(12061048)NSF of Jiangxi Province(20232BAB201026,20232BAB201018)。
文摘In this paper,a new technique is introduced to construct higher-order iterative methods for solving nonlinear systems.The order of convergence of some iterative methods can be improved by three at the cost of introducing only one additional evaluation of the function in each step.Furthermore,some new efficient methods with a higher-order of convergence are obtained by using only a single matrix inversion in each iteration.Analyses of convergence properties and computational efficiency of these new methods are made and testified by several numerical problems.By comparison,the new schemes are more efficient than the corresponding existing ones,particularly for large problem sizes.
文摘The bipolar plate serves a crucial function in the PEMFC regarding fuel supply quality and electrochemical performance.Achieving uniform gas distribution in the flow field presents significant challenges,particularly in transition areas where channel configurations directly affect reactant gas transport.This study presents a novel cathode flow field design for bipolar plates featuring multiple parallel sub-channels,analyzed through computational fluid dynamics modeling and simulation,with particular attention to the Coanda effect in the transition area.The investigation evaluates and analyzes mass flow distribution and pressure loss characteristics.The flow channels undergo further refinement through optimization of the transition region structure to achieve uniform mass flow distribution and reduced pressure loss.The results demonstrate that channel width allocation based on uniform distribution principles enhances mass flow uniformity.Additionally,incorporating fillets in the transition region with a jet width to fillet radius ratio below 0.5 amplifies the Coanda effect,thereby reducing adverse pressure gradients and flow separation phenomena,resulting in decreased pressure drop.Furthermore,when modifying gas outlet pressure to minimize pressure loss,an adjustment strategy for gas reactant concentration maintains consistent mass flow rates.The findings provide valuable reference points and optimization guidelines for flow channel design in bipolar plates,considering both fluid distribution uniformity and pressure loss factors.
基金supported by the Natural Science Foundation of Xinjiang Uygur Autonomous Region(Grant No.2022D01B 187)。
文摘Federated learning(FL)is a distributed machine learning paradigm for edge cloud computing.FL can facilitate data-driven decision-making in tactical scenarios,effectively addressing both data volume and infrastructure challenges in edge environments.However,the diversity of clients in edge cloud computing presents significant challenges for FL.Personalized federated learning(pFL)received considerable attention in recent years.One example of pFL involves exploiting the global and local information in the local model.Current pFL algorithms experience limitations such as slow convergence speed,catastrophic forgetting,and poor performance in complex tasks,which still have significant shortcomings compared to the centralized learning.To achieve high pFL performance,we propose FedCLCC:Federated Contrastive Learning and Conditional Computing.The core of FedCLCC is the use of contrastive learning and conditional computing.Contrastive learning determines the feature representation similarity to adjust the local model.Conditional computing separates the global and local information and feeds it to their corresponding heads for global and local handling.Our comprehensive experiments demonstrate that FedCLCC outperforms other state-of-the-art FL algorithms.
基金supported by the National Natural Science Foundation of China(Grant Nos.52425211,52272360,and 52472394)Chongqing Natural Science Foundation(CSTB2023NSCQ-MSX0300)。
文摘Trans-medium flight vehicles can combine high aerial maneuverability and underwater concealment ability,which have attracted much attention recently.As the most crucial procedure,the trajectory design generally determines the trans-medium flight vehicle performance.To quantitatively analyze the flight vehicle performance,an entire aerial-aquatic trajectory model is developed in this paper.Different from modeling a trajectory purely for the water entry process,the constructed entire trajectory model has integrated aerial,water entry,and underwater trajectories together,which can consider the influence of the connected trajectories.As for the aerial and underwater trajectories,explicit dynamic models are established to obtain the trajectory parameters.Due to the complicated fluid force during high-velocity water entry,a computational fluid dynamics model is investigated to analyze this phase.The compu-tational domain size is adaptively refined according to the final aerial trajectory state,where the redundant computational domain is removed.An entire trajectory optimization problem is then formulated to maximize the total flight range via tuning the joint states of different trajectories.Simultaneously,several constraints,i.e.,the max impact load,trajectory height,etc.,are involved in the optimization problem.Rather than directly optimizing by a heuristic algorithm,a multi-surrogate cooperative sampling-based optimization method is proposed to alleviate the computational complexity of the entire trajectory optimization problem.In this method,various surrogates coopera-tively generate infill sample points,thereby preventing the poor approximation.After optimization,the total flight range can be improved by 20%,while all the constraints are satisfied.The result demonstrates the effectiveness and practicability of the developed model and optimization framework.
基金Projects(52372402,U2568224)supported by the National Natural Science Foundation of China。
文摘With increasingly stringent requirements for the airtightness of high-speed train bodies,determining appropriate airtightness levels has become critically important.To calculate the airtightness of high-speed train bodies more accurately,based on one-dimensional isentropic flow theory,this study derives cabin pressure calculation models for both positive and negative pressure conditions during static airtightness tests of high-speed train bodies.Since the flow coefficient,which is closely related to the leakage characteristics of the carriage,is influenced by multiple factors including operating pressure conditions(positive/negative),leakage path cross-sectional shape,and size,a flow coefficient calibration method is proposed to achieve high-precision and efficient calibration of the flow coefficient for trains with varying leakage properties.This method generates a series of flow coefficient values for circular and square cross-sectional shapes under both positive and negative pressure conditions across various cross-sectional areas.Furthermore,functional relationships between flow coefficient and leakage path area under positive/negative pressure are established through curve fitting.Using these functional relationships and the cabin pressure calculation model,the pressure variation curves for a static airtightness test are simulated.Specifically,for circular cross-sectional shapes,the theoretical curves under positive and negative pressure conditions exhibited R^(2) values of 0.9936 and 0.9931,respectively,when compared to experimental data,and for square cross-sectional shapes,the corresponding R^(2) values are 0.9928 and 0.9932,validating the accuracy of the proposed theoretical model.The proposed theoretical model effectively evaluates the airtightness of high-speed train bodies with varying performance levels during static airtightness tests,providing a robust theoretical reference for optimizing high-speed train airtightness design.
基金Project(U24B20125)supported by the National Natural Science Foundation of ChinaProject(K2024T005)supported by the China National Railway Group Science and Technology Program。
文摘The suspension gap is a critical operational parameter for high-speed maglev trains and significantly impacts their aerodynamic performance.Based on an engineering prototype of the high-temperature superconducting(HTS)pinning maglev train,this study established a detailed three-dimensional model,and then the aerodynamic characteristics of the HTS maglev train at 600 km/h with suspension gaps of 10 mm,20 mm,and 30 mm were simulated based on the improved delayed detached eddy simulation(IDDES)turbulence model and SST kωtwo-equation.The results demonstrated that the underbody design of the HTS maglev train leads to unique aerodynamic drag and aerothermal distribution phenomena.The head car experiences the smallest drag,while the tail car experiences the largest.The aerothermal temperature on the train's bottom surface progressively increases from the head to the tail.Additionally,the U-shaped track significantly constrains the flow around the train body,forming strong vortex structures.As the suspension gap increases from 10 mm to 30 mm,the airflow velocity in the train-track gap rises,reducing the underbody pressure and decreasing the lift of the head car by 12.43%.The drag of the head car increases by 10.98%,primarily due to changes in pressure drag.Additionally,the temperature at the underbody of the tail car rises further due to significant airflow deceleration.These findings provide valuable insights for advancing the engineering design and application of the high-speed HTS maglev technology.
